





"""simple docstring"""





import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
       # Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
       # setup instructions, if using on-demand hardware

       # If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
       # If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
       # Throw an error if user passes both BYO and on-demand cluster args
       # Otherwise, use default values
       SCREAMING_SNAKE_CASE__   =  argparse.ArgumentParser()
       parser.add_argument("--user", type=str, default="ubuntu")
       parser.add_argument("--host", type=str, default="localhost")
       parser.add_argument("--key_path", type=str, default=None)
       parser.add_argument("--instance", type=str, default="V100:1")
       parser.add_argument("--provider", type=str, default="cheapest")
       parser.add_argument("--use_spot", type=bool, default=False)
       parser.add_argument("--example", type=str, default="pytorch/text-generation/run_generation.py")
       SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__   =  parser.parse_known_args()
       if args.host != "localhost":
              if args.instance != "V100:1" or args.provider != "cheapest":
                     raise ValueError("Cannot specify both BYO and on-demand cluster args")
              SCREAMING_SNAKE_CASE__   =  rh.cluster(
                  name="rh-cluster", ips=[args.host], ssh_creds={"ssh_user": args.user, "ssh_private_key": args.key_path}
              )
       else:
              SCREAMING_SNAKE_CASE__   =  rh.cluster(
                  name="rh-cluster", instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
              )
       SCREAMING_SNAKE_CASE__   =  args.example.rsplit("/", 1)[0]

       # Set up remote environment
       cluster.install_packages(["pip:./"])  # Installs transformers from local source
       # Note transformers is copied into the home directory on the remote machine, so we can install from there
       cluster.run([f'pip install -r transformers/examples/{example_dir}/requirements.txt'])
       cluster.run(["pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117"])

       # Run example. You can bypass the CLI wrapper and paste your own code here.
       cluster.run([f'python transformers/examples/{args.example} {" ".join(shlex.quote(arg) for arg in unknown)}'])

       # Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
       # from my_script... import train
       # reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
       # launch_train_gpu = rh.function(fn=train,
       #                                system=gpu,
       #                                reqs=reqs,
       #                                name='train_bert_glue')
       #
       # We can pass in arguments just like we would to a function:
       # launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
       #                  stream_logs=True)













"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__   =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   =  {
    "uclanlp/visualbert-vqa": "https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json",
    "uclanlp/visualbert-vqa-pre": "https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json",
    "uclanlp/visualbert-vqa-coco-pre": (
        "https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json"
    ),
    "uclanlp/visualbert-vcr": "https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json",
    "uclanlp/visualbert-vcr-pre": "https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json",
    "uclanlp/visualbert-vcr-coco-pre": (
        "https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json"
    ),
    "uclanlp/visualbert-nlvr2": "https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json",
    "uclanlp/visualbert-nlvr2-pre": "https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json",
    "uclanlp/visualbert-nlvr2-coco-pre": (
        "https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json"
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}

class 						lowercase   (   _UpperCAmelCase   ):
      _SCREAMING_SNAKE_CASE							      = 'visual_bert'





      def __init__(			self ,		lowercase=30_522 ,		lowercase=768 ,		lowercase=512 ,		lowercase=12 ,		lowercase=12 ,		lowercase=3_072 ,		lowercase="gelu" ,		lowercase=0.1 ,		lowercase=0.1 ,		lowercase=512 ,		lowercase=2 ,		lowercase=0.02 ,		lowercase=1e-12 ,		lowercase=False ,		lowercase=True ,		lowercase=1 ,		lowercase=0 ,		lowercase=2 ,		**lowercase ,		)						->	int:
            super().__init__(pad_token_id=lowercase ,		bos_token_id=lowercase ,		eos_token_id=lowercase ,		**lowercase					)

            lowerCAmelCase    		=			vocab_size
            lowerCAmelCase    		=			max_position_embeddings
            lowerCAmelCase    		=			hidden_size
            lowerCAmelCase    		=			visual_embedding_dim
            lowerCAmelCase    		=			num_hidden_layers
            lowerCAmelCase    		=			num_attention_heads
            lowerCAmelCase    		=			intermediate_size
            lowerCAmelCase    		=			hidden_act
            lowerCAmelCase    		=			hidden_dropout_prob
            lowerCAmelCase    		=			attention_probs_dropout_prob
            lowerCAmelCase    		=			initializer_range
            lowerCAmelCase    		=			type_vocab_size
            lowerCAmelCase    		=			layer_norm_eps
            lowerCAmelCase    		=			bypass_transformer
            lowerCAmelCase    		=			special_visual_initialize






